NDLI: No-reference quality measure in brain MRI images using binary operations, texture and set analysis

Content Provider	IET Digital Library
Author	Osadebey, Michael Pedersen, Marius Arnold, Douglas Mitoraj, Katrina Wendel
Abstract	The authors propose a new application-specific, post-acquisition quality evaluation method for brain magnetic resonance imaging (MRI) images. The domain of a MRI slice is regarded as universal set. Four feature images; greyscale, local entropy, local contrast and local standard deviation are extracted from the slice and transformed into the binary domain. Each feature image is regarded as a set enclosed by the universal set. Four qualities attribute; lightness, contrast, sharpness and texture details are described by four different combinations of feature sets. In an ideal MRI slice, the four feature sets are identically equal. Degree of distortion in real MRI slice is quantified by fidelity between the sets that describe a quality attribute. Noise is the fifth quality attribute and is described by the slice Euler number region property. Total quality score is the weighted sum of the five quality scores. The authors' proposed method addresses current challenges in image quality evaluation. It is simple, easy-to-use and easy-to-understand. Incorporation of binary transformation in the proposed method reduces computational and operational complexity of the algorithm. They provide experimental results that demonstrate efficacy of their proposed method on good quality images and on common distortions in MRI images of the brain.
Starting Page	672
Ending Page	684
Page Count	13
ISSN	17519659
Volume Number	11
e-ISSN	17519667
Issue Number	Issue 9, Sep (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/11/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.0560
Journal	IET Image Processing
Publisher Date	2017-04-13
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Application-specific Post-acquisition Quality Evaluation Method Binary Operations Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Brain Brain Magnetic Resonance Imaging Image Brain MRI Image Computational Complexity Computational Complexity Reduction Computer Vision And Image Processing Technique Entropy Euler Number Region Feature Extraction Feature Image Greyscale Image Image Contrast Image Quality Evaluation Image Recognition Image Sharpness Image Texture Lightness Local Contrast Local Entropy Local Standard Deviation Medical Magnetic Resonance Imaging And Spectroscopy MRI Image Distortion MRI Slice No-reference Quality Measure Operational Complexity Reduction Quality Attribute Quality Score Set Analysis Spectroscopy Texture Details
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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